Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
GROUP BEHAVIOR RECOGNITION WITH MULTIPLE CAMERAS
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Hidden Markov Models for Optical Flow Analysis in Crowds
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Modelling Crowd Scenes for Event Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Machine Vision and Applications
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Tracking in dense crowds using prominence and neighborhood motion concurrence
Image and Vision Computing
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Video surveillance in crowded areas is becoming more and more significant for public security. This paper presents a method for the detection of abnormality in crowded scenes based on the crowd motion characteristics. These characteristics includes the crowd kinetic energy and the motion directions. This approach estimates the crowd kinetic energy and the motion directions based on the optical flow techniques. The motion variation is derived from the crowd kinetic energy of two adjacent frames, and the motion direction variation is estimated using mutual information of the direction histograms of two neighboring motion vector fields. The proposed method combines crowd kinetic energy, motion variation and direction variation for the abnormality detection. The experiments on the video data which captured by ourselves demonstrate that our method can detect the abnormal behaviors effectively.